Quantitative analysis. We compare the mean average precision (mAP) of 3D bounding boxes across various difficulty settings for the domain shift scenarios Waymo \(\rightarrow \)  KITTI, nuScenes\(\rightarrow \)  KITTI, KITTI \(\rightarrow \)  nuScenes, and Waymo\(\rightarrow \)  nuScenes. Where the target dataset is KITTI, we use the official evaluation metrics detailed in [1]} with easy, moderate, and hard difficulty categories based on the distance and level of occlusion of the object from the sensor, with an IoU threshold of \(0.7\) . Where the target dataset is nuScenes, we use the metrics of [2]}, and average across difficulties to be consistent with both [3]} and [4]}. We reproduce the results of ST3D and SN using similar training lengths and batch sizes as our experiments. Note that due to computational limitations, the batch sizes are smaller than those used by [3]} and [6]}. Due to the lack of a code repository for SF-UDA3D at the time of writing, we compare with the reported numbers. We implement our method with SECOND-iou for comparison with ST3D and with PointRCNN for comparison with SF-UDA3D to be consistent with their base object detector networks. This can be seen in Table REF . We demonstrate the best results using both object detection networks in most categories, beating the weakly supervised approach of SN as well as [4]} and [3]}. We observe a \(19.95\%\)  overall improvement from the closest performing competitor ST3D in the case of SECOND-iou in the nuScenes \(\rightarrow \)  KITTI domain scenario and a \(5.76\%\) 
improvement in the case of Waymo \(\rightarrow \)  KITTI.
